A Mathematical Model For Optimal Decisions In A Representative Democracy
Authors: Malik Magdon-Ismail, Lirong Xia
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a mathematical model for studying representative democracy, in particular understanding the parameters of a representative democracy that gives maximum decision making capability. Our main result states that under general and natural conditions, 1. for fixed voting cost, the optimal number of representatives is linear; 2. for polynomial cost, the optimal number of representatives is logarithmic. |
| Researcher Affiliation | Academia | Malik Magdon-Ismail Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 magdon@cs.rpi.edu Lirong Xia Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180 xial@cs.rpi.edu |
| Pseudocode | No | The paper does not contain any pseudocode or explicitly labeled algorithm blocks. It focuses on mathematical models, theorems, and proofs. |
| Open Source Code | No | The paper does not mention or provide access to any open-source code. |
| Open Datasets | No | The paper describes a mathematical model and theoretical analysis, not experiments on a publicly available dataset. It uses theoretical distributions like "Uniform[a, b]" for its examples. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | The paper describes a mathematical model and theoretical findings, not computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical mathematical modeling and does not mention any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and presents mathematical proofs and models. It does not describe an experimental setup with hyperparameters or system-level training settings. |